Infectious Diseases: What You May Have Missed in 2022
Bibliographic record
Abstract
In 2022, COVID-19 remained the infectious disease at the top of most internal medicine physicians' minds. However, it was not the only infectious disease that was the topic of clinically relevant research that year. This article highlights some important infectious disease evidence unrelated to COVID-19 that was published in 2022. The literature was screened for sound new evidence relevant to internal medicine specialists and subspecialists whose focus of practice is not infectious diseases. The publications highlighted relate to various organisms in different patient populations. One article provides insight into the role of Helicobacter pylori eradication in the treatment of functional dyspepsia. The descriptive epidemiology of bacterial (Staphylococcus aureus) and viral (mpox) infections are the focus of 2 other articles. Several articles address the management of resistant and difficult-to-treat infections: multidrug-resistant gram-negative infections, resistant HIV-1, rifampin-resistant tuberculosis, cryptococcal meningitis, and invasive fungal infection in the setting of neutropenia. Another article provides data on effective HIV preexposure prophylaxis in women, an understudied population. Finally, given the urgent need to reduce inappropriate use of antibiotics, an article on antibiotic stewardship for hospitalized patients with presumed sepsis in a non–intensive care unit setting is also included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.045 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".